scholarly journals MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies

2013 ◽  
Vol 6 (1) ◽  
pp. 1259-1365 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Carbon Cycle ◽  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
World Ocean ◽  
Ecosystem Dynamics ◽  
Full Description ◽  
Biogeochemical Model ◽  
Intermediate Complexity ◽  
Nutrient Utilisation

Abstract. MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) was developed as an "intermediate complexity" plankton ecosystem model to study the biogeochemical response, and especially that of the so-called "biological pump", to anthropogenically-driven change in the World Ocean (Yool et al., 2011). The base currency in this model was nitrogen from which fluxes of organic carbon, including export to the deep ocean, were calculated by invoking fixed C:N ratios in phytoplankton, zooplankton and detritus. Since the beginning of the industrial era, the atmospheric concentration of carbon dioxide (CO2) has significantly increased above its natural, inter-glacial background concentration. Simulating and predicting the carbon cycle in the ocean in its entirety, including ventilation of CO2 with the atmosphere and the resulting impact of ocean acidification on marine ecosystems, therefore requires that both organic and inorganic carbon be afforded a full representation in the model specification. Here, we introduce MEDUSA-2.0, an expanded successor model which includes additional state variables for dissolved inorganic carbon, alkalinity, dissolved oxygen and detritus carbon (permitting variable C:N in exported organic matter), as well as a simple benthic formulation and extended parameterisations of phytoplankton growth, calcification and detritus remineralisation. A full description of MEDUSA-2.0, including its additional functionality, is provided and a multi-decadal hindcast simulation described (1860–2005), to evaluate the biogeochemical performance of the model.

scholarly journals MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies

2013 ◽  
Vol 6 (5) ◽  
pp. 1767-1811 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Carbon Cycle ◽  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Ecosystem Dynamics ◽  
Anthropogenic Activity ◽  
Full Description ◽  
Biogeochemical Model ◽  
Diverse Range ◽  
Intermediate Complexity ◽  
Nutrient Utilisation

Abstract. MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) was developed as an "intermediate complexity" plankton ecosystem model to study the biogeochemical response, and especially that of the so-called "biological pump", to anthropogenically driven change in the World Ocean (Yool et al., 2011). The base currency in this model was nitrogen from which fluxes of organic carbon, including export to the deep ocean, were calculated by invoking fixed C:N ratios in phytoplankton, zooplankton and detritus. However, due to anthropogenic activity, the atmospheric concentration of carbon dioxide (CO2) has significantly increased above its natural, inter-glacial background. As such, simulating and predicting the carbon cycle in the ocean in its entirety, including ventilation of CO2 with the atmosphere and the resulting impact of ocean acidification on marine ecosystems, requires that both organic and inorganic carbon be afforded a more complete representation in the model specification. Here, we introduce MEDUSA-2.0, an expanded successor model which includes additional state variables for dissolved inorganic carbon, alkalinity, dissolved oxygen and detritus carbon (permitting variable C:N in exported organic matter), as well as a simple benthic formulation and extended parameterizations of phytoplankton growth, calcification and detritus remineralisation. A full description of MEDUSA-2.0, including its additional functionality, is provided and a multi-decadal spin-up simulation (1860–2005) is performed. The biogeochemical performance of the model is evaluated using a diverse range of observational data, and MEDUSA-2.0 is assessed relative to comparable models using output from the Coupled Model Intercomparison Project (CMIP5).

Long-term variation in dissolved inorganic carbon and ocean acidification indices in the Northwest Pacific from 1993 to 2018: A study of a biogeochemical model with an operational ocean model product and observational evidence

2021 ◽  
Author(s):  
Miho Ishizu ◽  
Yasumasa Miyazawa ◽  
Xinyu Guo
Keyword(s):  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
Kuroshio Extension ◽  
Vertical Mixing ◽  
Ocean Model ◽  
Biogeochemical Model ◽  
Northwest Pacific ◽  
Subarctic Region ◽  
Surface Ocean

Abstract The multi-decadal variation in ocean acidification indices in the Northwest Pacific was examined using a biogeochemical model with an operational ocean model product for the period 1993–2018. We found that ocean acidification varied regionally in the Northwest Pacific. The surface ocean (above 100 m depth) underwent acidification that progressed more quickly in the subtropical region and the Kuroshio extension than in the subarctic region due to vertical mixing of the dissolved inorganic carbon (DIC) supply exceeding DIC release by air–sea exchange. Below 100 m depth, acidification and alkalinization occurred in the subtropical and subarctic regions, respectively. We attribute these regional differences in acidification and alkalinization to spatially variable biological processes in the upper layer and physical redistribution of DIC, both horizontally and vertically.

scholarly journals <i>MEDUSA</i>: a new intermediate complexity plankton ecosystem model for the global domain

2010 ◽  
Vol 3 (4) ◽  
pp. 1939-2019 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Deep Ocean ◽  
Ecosystem Model ◽  
Global Scale ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Full Description ◽  
State Variables ◽  
Intermediate Complexity ◽  
Nutrient Utilisation ◽  
Parameter Values

Abstract. The ongoing, anthropogenically-driven changes to the global ocean are expected to have significant consequences for plankton ecosystems in the future. Because of the role that plankton play in the ocean's "biological pump", changes in abundance, distribution and productivity will likely have additional consequences for the wider carbon cycle. Just as in the terrestrial biosphere, marine ecosystems exhibit marked diversity in species and functional types of organisms. Predicting potential change in plankton ecosystems therefore requires the use of models that are suited to this diversity, but whose parameterisation also permits robust and realistic functional behaviour. In the past decade, advances in model sophistication have attempted to address diversity, but have been criticised for doing so inaccurately or ahead of a requisite understanding of underlying processes. Here we introduce MEDUSA (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification), a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplankton-detritus (NPZD) models, and remains amenable to global-scale evaluation. MEDUSA includes the biogeochemical cycles of nitrogen, silicon and iron, broadly structured into "small" and "large" plankton size classes, of which the "large" phytoplankton class is representative of a key phytoplankton group, the diatoms. A full description of MEDUSA's state variables, differential equations, functional forms and parameter values is included, with particular attention focused on the submodel describing the export of organic carbon from the surface to the deep ocean. MEDUSA is used here in a multi-decadal hindcast simulation, and its biogeochemical performance evaluated at the global scale.

scholarly journals Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain

2011 ◽  
Vol 4 (2) ◽  
pp. 381-417 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Deep Ocean ◽  
Ecosystem Model ◽  
Global Scale ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Full Description ◽  
State Variables ◽  
Intermediate Complexity ◽  
Nutrient Utilisation ◽  
Parameter Values

Abstract. The ongoing, anthropogenically-driven changes to the global ocean are expected to have significant consequences for plankton ecosystems in the future. Because of the role that plankton play in the ocean's "biological pump", changes in abundance, distribution and productivity will likely have additional consequences for the wider carbon cycle. Just as in the terrestrial biosphere, marine ecosystems exhibit marked diversity in species and functional types of organisms. Predicting potential change in plankton ecosystems therefore requires the use of models that are suited to this diversity, but whose parameterisation also permits robust and realistic functional behaviour. In the past decade, advances in model sophistication have attempted to address diversity, but have been criticised for doing so inaccurately or ahead of a requisite understanding of underlying processes. Here we introduce MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification), a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplankton-detritus (NPZD) models, and remains amenable to global-scale evaluation. MEDUSA-1.0 includes the biogeochemical cycles of nitrogen, silicon and iron, broadly structured into "small" and "large" plankton size classes, of which the "large" phytoplankton class is representative of a key phytoplankton group, the diatoms. A full description of MEDUSA-1.0's state variables, differential equations, functional forms and parameter values is included, with particular attention focused on the submodel describing the export of organic carbon from the surface to the deep ocean. MEDUSA-1.0 is used here in a multi-decadal hindcast simulation, and its biogeochemical performance evaluated at the global scale.

scholarly journals Development of the UKESM-TOPAZ Earth System Model (Version 1.0) and Preliminary Evaluation of its Biogeochemical Simulations

2021 ◽  
Author(s):  
Hyomee Lee ◽  
Byung-Kwon Moon ◽  
Hyun-Chae Jung ◽  
Jong-Yeon Park ◽  
Sungbo Shim ◽  
...  
Keyword(s):  
Dissolved Inorganic Carbon ◽  
Initial Conditions ◽  
Southern Oscillation ◽  
Chlorophyll Concentration ◽  
Ecosystem Dynamics ◽  
Biogeochemical Model ◽  
Preliminary Evaluation ◽  
Earth System ◽  
Simulation Performance ◽  
Nutrient Utilisation

AbstractEarth system models (ESMs) comprise various Earth system components and simulate the interactions between these components. ESMs can be used to understand climate feedbacks between physical, chemical, and biological processes and predict future climate. We developed a new ESM, UKESM-TOPAZ, by coupling the UK ESM (UKESM1) and the Tracers of Phytoplankton with Allometric Zooplankton (TOPAZ) biogeochemical module. We then compared the preliminary simulated biogeochemical variables, which were conducted over a period of 70 years, using observational and existing UKESM1 model data. Similar to UKESM1, the newly developed UKESM-TOPAZ closely simulated the relationship between the El Niño-Southern Oscillation and chlorophyll concentration anomalies during the boreal winter. However, there were differences in the chlorophyll distributions in the eastern equatorial Pacific between the two models, which were due to dissolved iron, as this value was higher in UKESM-TOPAZ than in UKESM1. In a mean field analysis, the distributions of the major marine biogeochemical variables in UKESM-TOPAZ (i.e., nitrate, silicate, dissolved oxygen, dissolved inorganic carbon, and alkalinity) were not significantly different from those of UKESM1, likely because the models share the same initial conditions. Our results indicate that TOPAZ has a simulation performance that does not lag behind UKESM1’s basic biogeochemical model (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification; MEDUSA). The UKESM-TOPAZ model can simulate the variability of the observed Niño 3.4 and 4 indices more closely than UKESM1. Thus, the UKESM-TOPAZ model can be used to deepen our understanding of the Earth system and to estimate ESM uncertainty.

scholarly journals A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

2020 ◽  
Author(s):  
Daniel Broullón ◽  
Fiz F. Pérez ◽  
Antón Velo Lanchas ◽  
Mario Hoppema ◽  
Are Olsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Carbon Cycle ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
World Ocean ◽  
National Research Council ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Total Dissolved Inorganic Carbon

Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than two centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2) which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont-Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former’s variability. The global root-mean-squared error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO in capturing the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in ten time-series stations spread over different regions of the ocean (RMSE: 3.6 to 13.1 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity and oxygen from World Ocean Atlas 2013, and phosphate, nitrate and silicate computed from a neural network fed with the previous fields. The resolution is 1º x 1º in the horizontal, 102 depth levels (0–5500 m) and monthly (0–1500 m) to annual (1550–5500 m), and it is centered in the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time-series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2016). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; http://dx.doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).

scholarly journals Two decades of inorganic carbon dynamics along the Western Antarctic Peninsula

2015 ◽  
Vol 12 (9) ◽  
pp. 6929-6969 ◽  
Author(s):  
C. Hauri ◽  
S. C. Doney ◽  
T. Takahashi ◽  
M. Erickson ◽  
G. Jiang ◽  
...  
Keyword(s):  
Ocean Acidification ◽  
Interannual Variability ◽  
Antarctic Peninsula ◽  
Ocean Circulation ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
World Ocean ◽  
Data Set ◽  
Wide Range ◽  
Melt Water

Abstract. We present 20 years of seawater inorganic carbon measurements collected along the western shelf and slope of the Antarctic Peninsula. Water column observations from summertime cruises and seasonal surface underway pCO2 measurements provide unique insights into the spatial, seasonal and interannual variability of the dynamic system. The discrete measurements from depths > 2000 m align well with World Ocean Circulation Experiment observations across the time-series and underline the consistency of the data set. Analysis shows large spatial gradients in surface alkalinity and dissolved inorganic carbon content, with a concomitant wide range of Ωarag from values < 1 up to 3.9. This spatial variability was mainly driven by increasing influence of biological productivity towards the southern end of the sampling grid and melt water input along the coast towards the northern end. Large inorganic carbon drawdown through biological production in summer caused high near-shore Ωarag despite glacial and sea-ice melt water input. In support of previous studies, we observed Redfield behavior of regional C / N nutrient utilization, while the C / P (80.5 ± 2.5) and N / P (11.7 ± 0.3) molar ratios were significantly lower than the Redfield elemental stoichiometric values. Seasonal predictions of Ωarag suggest that surface waters remained mostly supersaturated with regard to aragonite throughout the study. However, more than a third of the predictions for winters between 1999 and 2013 resulted in Ωarag < 1.3. Such low levels of Ωarag may have implications for important organisms such as pteropods. Despite large interannual variability, surface pCO2 measurements indicate a statistically significant increasing trend of up to 23 μatm per decade in fall and spring and a concomitant decreasing pH, pointing towards first signs of ocean acidification in the region. The combination of ongoing ocean acidification and freshwater input may soon provoke more unfavorable conditions than what the ecosystem experiences today.

scholarly journals A global monthly climatology of oceanic total dissolved inorganic carbon: a neural network approach

2020 ◽  
Vol 12 (3) ◽  
pp. 1725-1743
Author(s):  
Daniel Broullón ◽  
Fiz F. Pérez ◽  
Antón Velo ◽  
Mario Hoppema ◽  
Are Olsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Carbon Cycle ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
World Ocean ◽  
National Research Council ◽  
Total Alkalinity ◽  
Global Ocean ◽  
Total Dissolved Inorganic Carbon

Abstract. Anthropogenic emissions of CO2 to the atmosphere have modified the carbon cycle for more than 2 centuries. As the ocean stores most of the carbon on our planet, there is an important task in unraveling the natural and anthropogenic processes that drive the carbon cycle at different spatial and temporal scales. We contribute to this by designing a global monthly climatology of total dissolved inorganic carbon (TCO2), which offers a robust basis in carbon cycle modeling but also for other studies related to this cycle. A feedforward neural network (dubbed NNGv2LDEO) was configured to extract from the Global Ocean Data Analysis Project version 2.2019 (GLODAPv2.2019) and the Lamont–Doherty Earth Observatory (LDEO) datasets the relations between TCO2 and a set of variables related to the former's variability. The global root mean square error (RMSE) of mapping TCO2 is relatively low for the two datasets (GLODAPv2.2019: 7.2 µmol kg−1; LDEO: 11.4 µmol kg−1) and also for independent data, suggesting that the network does not overfit possible errors in data. The ability of NNGv2LDEO to capture the monthly variability of TCO2 was testified through the good reproduction of the seasonal cycle in 10 time series stations spread over different regions of the ocean (RMSE: 3.6 to 13.2 µmol kg−1). The climatology was obtained by passing through NNGv2LDEO the monthly climatological fields of temperature, salinity, and oxygen from the World Ocean Atlas 2013 and phosphate, nitrate, and silicate computed from a neural network fed with the previous fields. The resolution is 1∘×1∘ in the horizontal, 102 depth levels (0–5500 m), and monthly (0–1500 m) to annual (1550–5500 m) temporal resolution, and it is centered around the year 1995. The uncertainty of the climatology is low when compared with climatological values derived from measured TCO2 in the largest time series stations. Furthermore, a computed climatology of partial pressure of CO2 (pCO2) from a previous climatology of total alkalinity and the present one of TCO2 supports the robustness of this product through the good correlation with a widely used pCO2 climatology (Landschützer et al., 2017). Our TCO2 climatology is distributed through the data repository of the Spanish National Research Council (CSIC; https://doi.org/10.20350/digitalCSIC/10551, Broullón et al., 2020).

Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model 

2021 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Lionel Guidi ◽  
Jean-Olivier Irisson
Keyword(s):  
Organic Carbon ◽  
Carbon Cycle ◽  
Deep Ocean ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Carbon Export ◽  
Mixing Depth ◽  
Chl A ◽  
Biogeochemical Models

&lt;p&gt;A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO&lt;sub&gt;2&lt;/sub&gt; flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO&lt;sub&gt;2&lt;/sub&gt; emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO&lt;sub&gt;2 &lt;/sub&gt;can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO&lt;sub&gt;2&lt;/sub&gt; fluxes, and on the amount of CO&lt;sub&gt;2&lt;/sub&gt; emissions that remain in the atmosphere.&lt;/p&gt;&lt;p&gt;In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.&lt;/p&gt;

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